Agentic AI is an AI system designed to act independently, make decisions, and take actions to achieve specific goals with minimal or no human input. These AI agents understand objectives, plan steps, and use tools to complete tasks efficiently. Think of Agentic AI as an advanced digital assistant, it can break complex tasks into smaller actions, reason through challenges, and adapt to new situations. By automating repetitive workflows and enhancing decision-making, Agentic AI helps businesses improve productivity, reduce manual effort, and achieve smarter operations.
While traditional computer programs follow predefined steps, real-world problems are rarely that neat. They’re messy, dynamic, and often require reasoning, adaptability, and initiative, which can be described as a detective solving a case rather than a machine ticking boxes. That’s where Agentic AI comes in. It navigates complexity, makes decisions, and takes action effectively, just like a capable human would.
Signs Your Business Needs Agentic AI
You may benefit from Agentic AI if your operations involve tasks that:
Some business decisions aren’t black and white, they require nuance and human-like reasoning. Agentic AI excels in:
When rules and variables are too numerous or dynamic to code manually, Agentic AI can help:
Agentic AI can parse, analyze, and make sense of diverse, unstructured data sources
Computer programs usually struggle with these kinds of tasks, but Agentic AI could be a good fit for it.
This is a new open-source framework from Google designed to simplify the development of AI agents and multi-agent systems. It provides tools for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Based on requirements and needs, the following are types of ADK.
These agents, driven by Large Language Models (LLMs), excel at language understanding, logical reasoning, strategic planning, response generation, and autonomous tool selection, positioning them as a strong choice for tasks heavily reliant on natural language processing and flexible decision-making.
These specialized agents manage the execution order of other agents in predictable patterns like sequence, parallel, or loops, without relying on an LLM for flow control. They are ideal for structured processes needing consistent execution.
To meet highly specific application requirements, developers can build these Custom Agents by directly extending the Baseagent class, which allows for the implementation of unique operational logic, tailored control flows, and specialized integrations.
A Multi Agent System (MAS) is a computerized system composed of multiple intelligent agents that interact within a shared environment to achieve individual and/or collective goals. These agents are autonomous entities that can perceive their surroundings, make decisions, and take actions.
CrewAI: This framework facilitates the creation of systems capable of autonomously handling complex tasks such as extracting data from documents, searching for information online, and generating insightful recommendations by integrating with different tools and allowing for custom AI tool creation.
LangChain: It is an opensource Agentic AI framework for developing applications powered by large language models (LLMs). It enables the creation of sophisticated AI applications by chaining together LLMs with various tools and data sources.
LlamaIndex: LlamaIndex is an open-source tool that simplifies the creation of LLM applications, particularly those employing RAG, by facilitating the connection of language models to specific data for more contextually relevant and precise outputs.
LangGraph: LangGraph provides a robust way to construct intricate AI agent workflows with state management and precise control, utilizing graph structures and direct LLM agent interaction.
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Agentic AI refers to intelligent systems that can autonomously perceive, decide, and act to achieve specific goals.
Agentic AI automates workflows, enhances decision-making, boosts productivity, and improves customer experiences significantly.
Common technologies include Python, Large Language Model(LLM), and various automation frameworks for building AI agents.
Agentic AI is more adaptive and intelligent, making autonomous decisions rather than just following pre-set rules.
They break down complex tasks hierarchically, with high-level agents planning and delegating to specialized lower-level agents for execution.
Yes, using models like GPT-4o, function calling, and the Agents SDK, you can architect sophisticated multilevel agent systems.
While AI agents will automate many tasks, they are more likely to increase human capabilities and create new roles.
